Sequential Detection of Market Shocks With Risk-Averse CVaR Social Sensors
Bibliographic record
Abstract
This paper considers a statistical signal processing problem involving agent-based models of financial markets, which at a microlevel are driven by socially aware and risk-averse agents. These agents trade (buy or sell) stocks at each trading instant by using the decisions of all previous agents (social learning) in addition to a private (noisy) signal they receive on the value of the stock. We are interested in the following: (1) modelling the dynamics of these risk averse agents and (2) sequential detection of a market shock based on the behaviour of these agents. Structural results that characterize social learning under a risk measure, conditional value-at-risk (CVaR), are presented and formulation of the Bayesian change point detection problem is provided. The structural results exhibit two interesting properties: (1) risk averse agents herd more often than risk neutral agents and (2) the stopping set in the sequential detection problem is nonconvex. The framework is validated on data from the Yahoo! Tech Buzz game dataset and it is revealed that (1) the model identifies the value changes based on agent's trading decisions. (2) Reasonable quickest detection performance is achieved when the agents are risk-averse.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".